MFF-Net: Flood Detection from SAR Images Using Multi-Frequency and Fuzzy Uncertainty Fusion

Highlights: What are the main findings? MFF-Net integrates multi-scale spatial and multi-frequency feature extraction with a fuzzy uncertainty fusion module to suppress system noise in SAR images and enhance flood detection. Compared with the existing methods, this model demonstrates superior performance in flood detection tasks across multiple SAR flood datasets and still achieves commendable results on diverse tasks and data sources. What are the implications of the main findings? The multi-frequency, multi-scale attention mechanism uses wavelet transforms to refine frequency features, reducing SAR noise interference and improving pixel-level detection of subtle flood regions. Fuzzy decision-making in uncertainty fusion reduces system noise and enhances accurate identification of fine-grained flood areas. Synthetic Aperture Radar (SAR) images are highly valuable for detecting water surfaces characterized by low roughness and minimal microwave reflection, which makes them essential for flood detection. Despite these advantages, SAR imagery still faces inherent challenges, particularly systematic noise, which limits the accuracy of pixel-level flood detection and causes fine-grained flood areas to be easily overlooked. To tackle these challenges, this study proposes a novel flood detection algorithm, the multi-frequency fuzzy uncertainty fusion network (MFF-Net), which is built upon a multi-scale architecture. Particularly, the multi-frequency feature extraction module in MFF-Net extracts frequency features at different levels, which mitigate systematic noise in the SAR images and improve the accuracy of pixel-level flood detection. The fuzzy uncertainty fusion module further mitigates noise interference and more effectively detects subtle flood areas that may be overlooked. The combined effect of these modules significantly enhances the detection capability for fine-grained flood areas. Experiments validate the effectiveness of MFF-Net on SAR benchmarks, including the MMflood Dataset with 50.2% of IoU, the Sen1Floods11 Dataset with 45.07% of IoU, the ETCI 2021 Dataset with 44.35% and the SAR Poyang Lake Water Body Sample Dataset with 57.27% of IoU, respectively. In addition, it has also been tested on actual flood events.